Abstract: A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.

Abstract: A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing.

Abstract: A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing.

Abstract: A system for understanding navigational semantics via hypothesis generation and contextual analysis is disclosed. The system may, such as when examining and testing a software application, address the handling and resolution of constraint hypotheses in an uncertain environment, where potentially overlapping or conflicting suggestions are generated with various confidences. The system may utilize algorithmic and/or machine learning tools to identify consistent constraints for the software application with the highest levels of confidence. During operation, the system may continuously perform hypothesis testing on constraints generated by the system, which may result in the creation of new hypotheses yielding improved confidences. Feedback from the hypothesis testing may be provided to knowledge sources to improve the processing of information subsequently processed by the system. The system may construct complex constraints on multiple fields or functional transitions with associated confidences.

Abstract: A system for providing autonomous discovery of field or navigation constraints is disclosed. The system may parse text extracted from an output of an application and text extracted from documents internal to the system, external to the system, or a combination thereof. The parsed text may be correlated by the system with a field of the application, an order of operations to be conducted within the application, a functional transition of the application, or a combination thereof. Based on the correlation, the system may generate a constraint for the field of the application, the order of operations to be conducted within the application, the functional transition of the application, or a combination thereof. The system may conduct hypothesis testing on the constraint to increase a confidence value of the constraint. Moreover, the system may validate the constraint with the application being evaluated by the system.

Abstract: A system for autonomously testing a computing system is disclosed. The system parses data obtained from a variety of sources, and extracts source concepts from the parsed data to generate models for inclusion in a set of agglomerated models. The system interacts with and explores features of a software application being tested by the system. Outputs and information obtained from the interaction are utilized by the system to update the models or generate new models for the set of agglomerated models. The agglomerated models are utilized by the system to execute tests on the application to detect potential defects and conflicts. Detected defects and conflicts may be included in a report for review. Feedback on the defects and conflicts may be utilized to further update the agglomerated models. The agglomerated models are updated recursively as additional data is obtained, further interactions are performed, and further outputs are generated over time.

Abstract: A system for optimizing system resources and runtime during a testing procedure is disclosed. During operation, the system may perform hypothesis testing on an application under evaluation, such as performing hypothesis testing on suggested constraints for the application under evaluation, which may result in the creation of new hypotheses yielding improved confidences. This testing may be optimized by simplifying and/or reducing the number of hypothesis tests used during the testing process. To do so, the hypothesis tests may be sorted into sets, and a subset of these sorted hypothesis tests may be executed. Based on the results of this testing, a remaining portion of the hypothesis tests may be skipped, allowing for an optimization of the testing process without reducing the quality of the testing procedure.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.

Abstract: A system for autonomously testing a computing system is disclosed. The system parses data obtained from a variety of sources, and extracts source concepts from the parsed data to generate models for inclusion in a set of agglomerated models. The system interacts with and explores features of a software application being tested by the system. Outputs and information obtained from the interaction are utilized by the system to update the models or generate new models for the set of agglomerated models. The agglomerated models are utilized by the system to execute tests on the application to detect potential defects and conflicts. Detected defects and conflicts may be included in a report for review. Feedback on the defects and conflicts may be utilized to further update the agglomerated models. The agglomerated models are updated recursively as additional data is obtained, further interactions are performed, and further outputs are generated over time.